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Adaptive Neural Backstepping Control Approach for Tracker Design of Wheelchair Upper-Limb Exoskeleton Robot System

Author

Listed:
  • Ayman A. Aly

    (Department of Mechanical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia
    King Salman Center for Disability Research, Riyadh 94682, Saudi Arabia)

  • Kuo-Hsien Hsia

    (Department of Electrical Engineering, National Yunlin University of Science and Technology, 123 University Road, Douliou, Yunlin 64002, Taiwan)

  • Fayez F. M. El-Sousy

    (Department of Electrical Engineering, Prince Sattam Bin Abdulaziz University, Al Kharj 16278, Saudi Arabia)

  • Saleh Mobayen

    (Graduate School of Intelligent Data Science, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 640301, Taiwan)

  • Ahmed Alotaibi

    (Department of Mechanical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia
    King Salman Center for Disability Research, Riyadh 94682, Saudi Arabia)

  • Ghassan Mousa

    (King Salman Center for Disability Research, Riyadh 94682, Saudi Arabia
    Department of Mechanical Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Dac-Nhuong Le

    (King Salman Center for Disability Research, Riyadh 94682, Saudi Arabia
    Institute of Research and Development, Duy Tan University, Danang 550000, Vietnam
    School of Computer Science, Duy Tan University, Danang 550000, Vietnam)

Abstract

In this study, the desired tracking control of the upper-limb exoskeleton robot system under model uncertainty and external disturbance is investigated. For this reason, an adaptive neural network using a backstepping control strategy is designed. The difference between the actual values of the upper-limb exoskeleton robot system and the desired values is considered as the tracking error. Afterward, the auxiliary variable based on the tracking error is defined and the virtual control input is obtained. Then, by using the backstepping control procedure and Lyapunov stability concept, the convergence of the position tracking error is proved. Moreover, for the compensation of the model uncertainty and the external disturbance that exist in the upper-limb exoskeleton robot system, an adaptive neural-network procedure is adopted. Furthermore, for the estimation of the unknown coefficient related to the parameters of the neural network, the adaptive law is designed. Finally, the simulation results are prepared for demonstration of the effectiveness of the suggested method on the upper-limb exoskeleton robot system.

Suggested Citation

  • Ayman A. Aly & Kuo-Hsien Hsia & Fayez F. M. El-Sousy & Saleh Mobayen & Ahmed Alotaibi & Ghassan Mousa & Dac-Nhuong Le, 2022. "Adaptive Neural Backstepping Control Approach for Tracker Design of Wheelchair Upper-Limb Exoskeleton Robot System," Mathematics, MDPI, vol. 10(22), pages 1-16, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4198-:d:968156
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    References listed on IDEAS

    as
    1. Xiangjian Chen & Di Li & Pingxin Wang & Xibei Yang & Hongmei Li, 2020. "Model-Free Adaptive Sliding Mode Robust Control with Neural Network Estimator for the Multi-Degree-of-Freedom Robotic Exoskeleton," Complexity, Hindawi, vol. 2020, pages 1-10, March.
    2. Razzaghian, Amir, 2022. "A fuzzy neural network-based fractional-order Lyapunov-based robust control strategy for exoskeleton robots: Application in upper-limb rehabilitation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 567-583.
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    1. Razzaghian, Amir, 2022. "A fuzzy neural network-based fractional-order Lyapunov-based robust control strategy for exoskeleton robots: Application in upper-limb rehabilitation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 193(C), pages 567-583.
    2. Ayman A. Aly & Mai The Vu & Fayez F. M. El-Sousy & Ahmed Alotaibi & Ghassan Mousa & Dac-Nhuong Le & Saleh Mobayen, 2022. "Fuzzy-Based Fixed-Time Nonsingular Tracker of Exoskeleton Robots for Disabilities Using Sliding Mode State Observer," Mathematics, MDPI, vol. 10(17), pages 1-19, September.

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